STYLE cosmetic fixes in sklearn.mixture.gmm

ogrisel · ogrisel · commit 05ad7459f3bd · 2015-06-02T16:40:36.000+02:00
diff --git a/sklearn/mixture/gmm.py b/sklearn/mixture/gmm.py
@@ -33,16 +33,19 @@ def log_multivariate_normal_density(X, means, covars, covariance_type='diag'):
     X : array_like, shape (n_samples, n_features)
         List of n_features-dimensional data points.  Each row corresponds to a
         single data point.
+
     means : array_like, shape (n_components, n_features)
         List of n_features-dimensional mean vectors for n_components Gaussians.
         Each row corresponds to a single mean vector.
+
     covars : array_like
         List of n_components covariance parameters for each Gaussian. The shape
         depends on `covariance_type`:
             (n_components, n_features)      if 'spherical',
             (n_features, n_features)    if 'tied',
             (n_components, n_features)    if 'diag',
             (n_components, n_features, n_features) if 'full'
+
     covariance_type : string
         Type of the covariance parameters.  Must be one of
         'spherical', 'tied', 'diag', 'full'.  Defaults to 'diag'.
@@ -119,7 +122,6 @@ class GMM(BaseEstimator):
     Initializes parameters such that every mixture component has zero
     mean and identity covariance.
 
-
     Parameters
     ----------
     n_components : int, optional
@@ -182,8 +184,6 @@ class GMM(BaseEstimator):
     converged_ : bool
         True when convergence was reached in fit(), False otherwise.
 
-
-
     See Also
     --------
 
@@ -268,13 +268,15 @@ def __init__(self, n_components=1, covariance_type='diag',
 
     def _get_covars(self):
         """Covariance parameters for each mixture component.
-        The shape depends on `cvtype`::
 
-            (`n_states`, 'n_features')                if 'spherical',
-            (`n_features`, `n_features`)              if 'tied',
-            (`n_states`, `n_features`)                if 'diag',
-            (`n_states`, `n_features`, `n_features`)  if 'full'
-            """
+        The shape depends on ``cvtype``::
+
+            (n_states, n_features)                if 'spherical',
+            (n_features, n_features)              if 'tied',
+            (n_states, n_features)                if 'diag',
+            (n_states, n_features, n_features)    if 'full'
+
+        """
         if self.covariance_type == 'full':
             return self.covars_
         elif self.covariance_type == 'diag':
@@ -323,8 +325,8 @@ def score_samples(self, X):
             raise ValueError('The shape of X  is not compatible with self')
 
         lpr = (log_multivariate_normal_density(X, self.means_, self.covars_,
-                                               self.covariance_type)
-               + np.log(self.weights_))
+                                               self.covariance_type) +
+               np.log(self.weights_))
         logprob = logsumexp(lpr, axis=1)
         responsibilities = np.exp(lpr - logprob[:, np.newaxis])
         return logprob, responsibilities
@@ -420,8 +422,8 @@ def sample(self, n_samples=1, random_state=None):
         return X
 
     def fit_predict(self, X, y=None):
-        """
-        Fit and then predict labels for data.
+        """Fit and then predict labels for data.
+
         Warning: due to the final maximization step in the EM algorithm,
         with low iterations the prediction may not be 100% accurate
 
@@ -653,7 +655,7 @@ def aic(self, X):
 
 
 #########################################################################
-## some helper routines
+# some helper routines
 #########################################################################
 
 
@@ -684,8 +686,7 @@ def _log_multivariate_normal_density_tied(X, means, covars):
 
 
 def _log_multivariate_normal_density_full(X, means, covars, min_covar=1.e-7):
-    """Log probability for full covariance matrices.
-    """
+    """Log probability for full covariance matrices."""
     n_samples, n_dim = X.shape
     nmix = len(means)
     log_prob = np.empty((n_samples, nmix))
@@ -751,8 +752,7 @@ def _validate_covars(covars, covariance_type, n_components):
 
 def distribute_covar_matrix_to_match_covariance_type(
         tied_cv, covariance_type, n_components):
-    """Create all the covariance matrices from a given template
-    """
+    """Create all the covariance matrices from a given template"""
     if covariance_type == 'spherical':
         cv = np.tile(tied_cv.mean() * np.ones(tied_cv.shape[1]),
                      (n_components, 1))
